US7085692B2 - Learning systems and methods for market-based control of smart matter - Google Patents
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
Description
obtained from all N of the models, is split between the N models according to how well each model predicted the behavior of the system. For example, if the prediction error in the ith model is ei(t+Δ)=x(t+Δ)−x1(t), u(t), then the fraction of the amount
going to the ith model is
where σ2 is an estimate of the noise variance. That is, there should be an incentive to predict better than the noise. In this case, the new model weights would be given by the difference between the amount invested and the return on investment. In other words:
This preserves the fact that the weights sum to 1.
Moreover, each model may also have a number of adjustable parameters that can be revised to maximize the accuracy of the models' predictions. As a given model adjusts its investment strategy by varying one or more of the adjustable parameters, that model will be rewarded. Operation then continues to step S2000.
This modification would be less aggressive in changing weights to successive models but offer more stability in the presence of large noise fluctuations.
where ui(t) is the control action the ith controller would have taken by itself. The error between the desired state and the resulting state is used to adjust the control weighting according to the weight of the responsible controller. Controllers which have a large weights are more responsible for the overall error than ones with smaller weights. Therefore, such large weight controllers should receive correspondingly larger rewards and losses, i.e., correspondingly large increases and decreases in the control weights wi.
Claims (11)
xi(t+Δt;x(t),u(t)),
xi(t+Δt;x(t),u(t)),
xi(t+Δ;x(t),u(t)),
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Cited By (4)
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US20040059772A1 (en) * | 2002-06-27 | 2004-03-25 | Mathias Bieringer | Method and controller for program control of a computer program having multitasking capability |
US20070118236A1 (en) * | 2001-01-31 | 2007-05-24 | Carl Bennett | Method for improving a manufacturing process |
US20070135938A1 (en) * | 2005-12-08 | 2007-06-14 | General Electric Company | Methods and systems for predictive modeling using a committee of models |
US20110144818A1 (en) * | 2009-12-14 | 2011-06-16 | Cong Li | Method and apparatus for dynamically allocating power in a data center |
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US6925364B1 (en) * | 2003-02-13 | 2005-08-02 | Hewlett-Packard Development Company, L.P. | Power market approach for device cooling |
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US20040059772A1 (en) * | 2002-06-27 | 2004-03-25 | Mathias Bieringer | Method and controller for program control of a computer program having multitasking capability |
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